Project Summary Young adults with normal audiometric thresholds vary widely in their ability to listen in everyday noisy environments. Many perform as poorly in studies as their age-matched, hard-of-hearing counterparts [Kidd et al. (2002). J. Assoc. Res. Otolaryn. 3, 107-119]. The variation challenges the conventional view of hearing loss, which assumes the audiogram to be the gold standard for evaluating hearing; however, the causes of the variation remain unclear. Now, new developments in our lab and others promise progress in understanding. Measures of threshold fine structure have shown that wide individual variation in thresholds are missed by the conventional audiogram [Lee & Long (2012). Hear. Res. 283, 24-32]. Individual differences in decision weights, taken to reflect the reliance listeners place on different frequencies in psychophysical tasks, have been linked to irregularities in cochlear micromechanics unique to individual ears [Lee et al. (2016), Adv. Exp. Med. Biol. 894:457-465]. Computational models have isolated a small number of factors that show promise in predicting individual differences across diverse target-in-noise listening tasks [Lutfi et al. (2013), J. Acoust. Soc. Am. 134:2160-2170]. And, levels of noise exposure, once thought to present no risk of harm, have been shown to produce irreversible loss of synaptic connections to hair cells and subsequent degeneration of afferents in mice. The pathology is not detected by conventional audiometry, leading to speculation that it may be widespread in the population, affecting listening in noise [Kujawa and Liberman (2009), J. Neurosci. 29(45):14077?14085]. Sparked by these developments, this proposal represents a new effort to understand individual differences in the ability of young clinically normal-hearing adults to listen effectively in noise. It differs fundamentally from past efforts in approach. The goal is to isolate the primary sources of variation and model their effects by determining precisely how listeners differ in their use of information distinguishing targets from noise. This is achieved by (1) formulating a general decision model relating the listener's trial-by-trial judgments to this information, (2) estimating the parameters of this model by logistic regression of the trial-by- trial data and (3) comparing the estimated values to those of a maximum-likelihood observer that yields best performance for each task. The approach represents a significant advance over conventional methods that infer underlying processes from measures of performance accuracy. Specific aims are to apply this approach to (1) determine the relative contribution of peripheral and central processes and their interaction to individual differences, (2) account for general patterns of listener behavior from `individual listening styles' and (3) develop a low-parameter computational model for predicting individual differences across diverse listening tasks. It is expected that the knowledge gained from these studies will inform efforts that seek to improve the evaluation, classification and treatment of what is a debilitating problem for many, the challenge of listening in everyday noise.